How to extract keywords from Reviews.io Online Reviews using generative AI

Keyword Identification

Reviews.io

As a business owner, knowing the key themes and sentiments of your customer reviews is essential for improving your product, customer experience, and marketing. However, manually analyzing hundreds or thousands of reviews can be a daunting and time-consuming task. In this post, we will show you how to use generative AI to automatically extract keywords from Reviews.io online reviews, making it easier to identify the most important insights and themes.

What is Keyword Extraction?

Keyword extraction is a natural language processing (NLP) technique that involves identifying the most important and relevant words or phrases in a piece of text. It can be used to extract key information and themes from text, such as product features, customer sentiment, and common issues. Keyword extraction can be performed manually, but it can also be automated using machine learning algorithms.

Generative AI algorithms can learn to recognize patterns and features in the text that are associated with important words or phrases, and can be trained on a labeled dataset of text. By using generative AI to extract keywords from Reviews.io online reviews, you can save time and effort while still getting valuable insights into your customers’ experiences.

Example Use Cases

Here are some examples of how you can use keyword extraction from Reviews.io online reviews:

Identifying common customer issues and pain points
Monitoring product feedback and reviews over time
Measuring customer sentiment and satisfaction
Improving product features and customer experience
Comparing your product with competitors

Teams that might find these use cases helpful include: product, marketing, customer support, and customer success.

Accessing the Data and Identifying Preliminary Keywords

You can extract Reviews.io online reviews using their API or by exporting the data in CSV format. Once you have your data, you can use generative AI tools to identify and measure the frequency of keywords in the reviews.

It can be helpful to identify common keywords or themes that you want to extract from your reviews. For example, if you are a restaurant owner, you might want to extract keywords related to food quality, service, and atmosphere. By identifying these preliminary keywords, you can train your generative AI algorithm to extract them more accurately.

After identifying your preliminary keywords, you can use generative AI to extract keywords from Reviews.io online reviews. This will help you quickly identify the most important themes and sentiments in your customer reviews, allowing you to make data-driven decisions to improve your product and customer experience.

Conclusion

Using generative AI to extract keywords from Reviews.io online reviews can help you save time and effort while still getting valuable insights into your customers’ experiences. By identifying common themes and sentiments, you can make data-driven decisions to improve your product and customer experience. With the right tools and techniques, you can turn your customer reviews into a valuable source of business intelligence.

Using AirOps to perform Keyword Identification

With AirOps, you can easily extract relevant keywords and phrases from your text-based data using the Keyword Identifier data app. Here's how:

Select "Keyword Identifier" from the Data Apps page. The input required for Keyword Identifier is the "text_field" which is the input text data.
Decide where you want the analysis to be performed and stored. The Keyword Identifier data app can be easily used in the AirOps Data App page and via API, but in this example, the analysis will be performed in Snowflake through an external function called AIROPS_KEYWORD_IDENTIFIER.

Here is an example SQL query:
```
SELECT
AIROPS_KEYWORD_IDENTIFIER(text_field) as result
FROM
your_table
```
Execute the keyword extraction analysis by running the SQL query. The output will contain an array of keywords and phrases extracted from the input text data.

Example Input:

"Hello, I am having trouble with my account. I cannot seem to log in and I have tried resetting my password multiple times."

Example Output:
```
"keywords": ["trouble", "account", "log in", "resetting", "password", "multiple times"],"summary": "A customer is having trouble logging into their account and has tried resetting their password multiple times."
```

Using AirOps to perform Sentiment Analysis

With AirOps, you can easily perform sentiment analysis on any text data such as reviews, support tickets, or sales calls using Sentiment Analyzer. Here’s how:

Select "Sentiment Analyzer" from the Data Apps page. The only input for Sentiment Analyzer is some text to analyze.
Decide where you want the analysis to be performed and stored. The Sentiment Analyzer data app can be easily used in the AirOps Data App page and via API, but in this example, the analysis will be performed in Snowflake through an external function called AIROPS_SENTIMENT_ANALYZER.

Here is an example SQL query:
```
SELECT
AIROPS_SENTIMENT_ANALYZER(text_field) as result
FROM
your_table
```
Execute the sentiment analysis by running the SQL query. The output will contain a sentiment score and sentiment summary, as well as a list of positive and negative keywords extracted from the input text data.
Input:
"I'm sorry to say that I had a terrible experience with your product. The customer service was unresponsive and the product didn't work as advertised."
Output:
```
"positive_keywords": [],"negative_keywords": ["terrible experience", "customer service", "unresponsive", "product", "didn't work", "advertised"],"score": -0.8,"sentiment": "Very Negative"
```

Using AirOps to perform Text Classification

With AirOps, you can easily perform classification using generative AI. Here’s how:

Select "Text Classifier'' from the Data Apps page. Below are the possible inputs for Text Classifier.text_field: The input text data.categories (optional): Categories can be specified as a comma-separated list. Leave empty for automatic determination.multi_category: Set to “true” if the text can belong to multiple categories, or “false” if it can only belong to one category.
Decide where you want the analysis to be performed and stored. The Text Classifier data app can be easily used in the AirOps Data App page and via API, but in this example, the analysis will be performed in Snowflake through an external function called AIROPS_CLASSIFIER.

Here is an example SQL query:
```
SELECT
AIROPS_CLASSIFIER(text_field, categories, multi_category) as result
FROM
your_table
```
Execute the classification analysis by running the SQL query. The output will contain a list of keywords extracted from the input text data that are relevant to the identified categories and a list of categories that the input text data belongs to based on the provided categories or automatic determination.

If you have feedback, please let us know!